Delete obsolete rag_tools.py

rag_tools.py

@@ -1,88 +0,0 @@
-"""Tools used by the RAG agent.
-
-This module defines two LangChain tools:
-
-1. `search_local_kb` – semantic search in the Chroma vector store.
-2. `web_search` – web search using Tavily.
-
-Both tools are decorated with `@tool` so that they can be used by the agent.
-"""
-
-from typing import List
-
-from langchain_core.tools import tool
-from langchain_ollama import ChatOllama
-from langchain_tavily import TavilySearchResults
-from langchain_chroma import Chroma
-
-# Global LLM instance for tool responses (can be reused)
-_llm = ChatOllama(model="llama3")
-
-# ---------------------------------------------------------------------------
-# Local KB search tool
-# ---------------------------------------------------------------------------
-
-@tool("search_local_kb")
-
-def search_local_kb(query: str, top_k: int = 3, vectorstore: Chroma = None) -> str:
-    """Search the local Chroma vector store for relevant chunks.
-
-    Parameters
-    ----------
-    query: str
-        The user's query.
-    top_k: int, optional
-        Number of top results to return.
-    vectorstore: Chroma
-        The Chroma vector store instance.
-
-    Returns
-    -------
-    str
-        A formatted string containing the retrieved chunks.
-    """
-    if vectorstore is None:
-        raise ValueError("Vectorstore must be provided to search_local_kb tool.")
-    retriever = vectorstore.as_retriever(search_kwargs={"k": top_k})
-    docs = retriever.invoke(query)
-    # docs is a list of Document objects
-    if not docs:
-        return "No relevant information found in the local knowledge base."
-    # Concatenate the content of the top documents
-    snippets = [f"{i+1}. {doc.page_content[:200]}..." for i, doc in enumerate(docs)]
-    return "\n".join(snippets)
-
-# ---------------------------------------------------------------------------
-# Web search tool
-# ---------------------------------------------------------------------------
-
-@tool("web_search")
-
-def web_search(query: str, top_k: int = 3) -> str:
-    """Perform a web search using Tavily.
-
-    Parameters
-    ----------
-    query: str
-        The user's query.
-    top_k: int, optional
-        Number of results to return.
-
-    Returns
-    -------
-    str
-        A formatted string containing the search results.
-    """
-    tavily = TavilySearchResults(tavily_api_key=None, max_results=top_k)
-    results = tavily.invoke(query)
-    if not results:
-        return "No results found on the web."
-    snippets = [f"{i+1}. {res['title']} – {res['url']}" for i, res in enumerate(results)]
-    return "\n".join(snippets)
-
-# ---------------------------------------------------------------------------
-# Exported tool names for agent
-# ---------------------------------------------------------------------------
-
-TOOLS = [search_local_kb, web_search]
-""